aliriahi90,Ali Riahi Samani,github

machine-learning-classification-regression-twitter-buzz

Buzz Prediction on Twitter: Buzz Prediction on Twitter Project Description: There are two different datasets for Regression and Classification tasks. Right-most column in both the datasets is a dependent variable i.e. buzz. Data description files are also provided for both the datasets. Deciding which dataset is for which task is part of the project. Read data into Jupyter notebook, use pandas to import data into a data frame. Preprocess data: Explore data, check for missing data and apply data scaling. Justify the type of scaling used. Regression Task: Apply all the regression models you've learned so far. If your model has a scaling parameter(s) use Grid Search to find the best scaling parameter. Use plots and graphs to help you get a better glimpse of the results. Then use cross-validation to find average training and testing score. Your submission should have at least the following regression models: KNN regressor, linear regression, Ridge, Lasso, polynomial regression, SVM both simple and with kernels. Finally, find the best regressor for this dataset and train your model on the entire dataset using the best parameters and predict buzz for the test_set. Classification Task: Decide about a good evaluation strategy and justify your choice. Find best parameters for the following classification models: KNN classification, Logistic Regression, Linear Support Vector Machine, Kernelized Support Vector Machine, Decision Tree. Which model gives the best results? Buzz Prediction on Twitter Project Description: Use same datasets as Project 2. Run all the models only on 10% data. Use code given in Project 2 for sampling. Preprocess data: Explore data and apply data scaling. Regression Task: Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class Classification Task: Apply four voting classifiers - two with hard voting and two with soft voting Apply any two models with bagging and any two models with pasting. Apply any two models with adaboost boosting Apply one model with gradient boosting Apply PCA on data and then apply all the models in project 2 again on data you get from PCA. Compare your results with results in project 2. You don't need to apply all the models twice. Just copy the result table from project 2, prepare similar table for all the models after PCA and compare both tables. Does PCA help in getting better results? Apply deep learning models covered in class

nlp-finding-similar-sentences

The problem is generated by Quora, a platform to share general knowledge - about anything. Over 100 million people visit Quora every month and many people ask similarly worded questions. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question, also make writers answer similar questions multiple times.

nlp-speech-recognition

Part 1: Speech to text using Google api

trajectory-prediction

PyTorch code for the paper "FIERY: Future Instance Segmentation in Bird's-Eye view from Surround Monocular Cameras"

aliriahi90 Goto Github PK

Ali Riahi Samani's Projects

machine-learning-classification-regression-twitter-buzz

nlp-finding-similar-sentences

nlp-speech-recognition

trajectory-prediction

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